论文标题
DTDN：双任务脱损网络
DTDN: Dual-task De-raining Network
论文作者
论文摘要
对于计算机视觉中的许多任务（例如对象检测和识别），必须从雨水中删除雨条。它需要解决两个相互排斥的目标：删除雨条并保留现实的细节。平衡它们对于脱割方法至关重要。我们提出了一个称为双任务脱损网络（DTDN）的端到端网络，该网络由两个子网络组成：生成对抗网络（GAN）和卷积神经网络（CNN），以自我适应性地进行协调，以消除雨条。 DTDN-GAN主要用于删除结构雨条，而DTDN-CNN旨在恢复原始图像中的细节。我们还设计了一种培训算法，以培训DTDN的这两个子网络，它们具有相同的权重但使用不同的训练集。我们进一步丰富了两个现有数据集，以近似真实的雨条的分布。实验结果表明，基于基准测试数据集和真实的下雨图像，我们的方法的表现优于最近的几种最新方法。
Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition. It needs to address two mutually exclusive objectives: removing rain streaks and reserving realistic details. Balancing them is critical for de-raining methods. We propose an end-to-end network, called dual-task de-raining network (DTDN), consisting of two sub-networks: generative adversarial network (GAN) and convolutional neural network (CNN), to remove rain streaks via coordinating the two mutually exclusive objectives self-adaptively. DTDN-GAN is mainly used to remove structural rain streaks, and DTDN-CNN is designed to recover details in original images. We also design a training algorithm to train these two sub-networks of DTDN alternatively, which share same weights but use different training sets. We further enrich two existing datasets to approximate the distribution of real rain streaks. Experimental results show that our method outperforms several recent state-of-the-art methods, based on both benchmark testing datasets and real rainy images.
